Outliers Data Mining in Normal-Inverse Gaussian Model

摘要：

The normal-inverse model arises as a normal variancemean mixture with an inverse Gaussian mixing model. The resulting model, it is very complicated to obtain the influence measures based on the tradition method. In the present paper, several diagnostic measures for outlier data mining are obtained based on the conditional expectation of the complete-data loglikelihood function based on the EM algorithm. An example for which we apply the diagnosis methods is given as illustration.

关键词： EM algorithm generalized Cook distance local influence analysis normal inverse Gaussian model

作者: Li-li Wang Xiang-yang Hou Yan-ye Xiong

作者单位: School of Command Automation PLA University of Science and Technology Nanjing, China Scientific Rese Scientific Researching Department Navy Command College Nanjing, China

会议类型: 国际会议

会议名称: Third International Conference on Information and Computing(第三届信息与计算科学国际会议 ICIC 2010)

会议地点: 无锡

会议语种:英文

页码: 231-234

在线出版日期: 2010-06-04（万方平台首次上网日期，不代表论文的发表时间）